Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16052
Title: Energy efficiency maximisation in large scale MIMO systems
Authors: Panneer Selvan, Vaina Malar
Advisors: Al-Raweshidy, H
Abbod, M
Keywords: Analytical ZF processing;Energy model;Channel estimation;Linear processing;Monte Carlo simulation
Issue Date: 2017
Publisher: Brunel University London
Abstract: The power usage of the communication technology industry and the consistent energy-related pollution are becoming major societal and economic concerns. These concern stimulated academia and industry to an intense activity in the new research area of green cellular networks. Bandwidth Efficiency (BE) is one of the most important metrics to select candidate technologies for next-generation wireless communications systems. Nevertheless, the important goal is to design new innovative network architecture and technologies needed to encounter the explosive development in cellular data demand without increasing the power consumption. As a result, Energy Efficiently (EE) has become another significant metric for evaluating the performance of wireless communications systems. MIMO technology has drawn lots of attention in wireless communication, as it gives substantial increases in link range and throughput without an additional increase in bandwidth or transmits power. Multi-user MIMO (MU-MIMO) regarded when evolved Base Station equipped with multiple antennas communicates with several User Terminal (UEs) at the same time. MU-MIMO is capable of improving either the reliability or the BE by improving either the multiplexing gains or diversity gains. A proposed new idea in MU-MIMO refers to the system that uses hundreds of antennas to serve dozens of UEs simultaneously. This so-called, Large Scale-MIMO (LS MIMO) regarded as a candidate technique for future wireless communication systems. An analysis is conducted to investigate the performance of the proposed uplink and downlink of LS MIMO systems with different linear processing techniques at the base station. The most common precoding and receive combining are considered: minimum mean squared error (MMSE), maximum ratio transmission/combining (MRT/MRC), and zero-forcing (ZF)processing. The fundamental problems answered on how to select the number of (BS) antennas 𝑀, number of active (UEs) 𝐾, and the transmit power to cover a given area with maximal EE. The EE is defined as the number of bits transferred per Joule of energy. A new power consumption model is proposed to emphasise that the real power scales faster with 𝑀 and 𝐾 than scaling linearly. The new power consumption model is utilised for deriving closed-form EE maximising values of the number of BS antennas, number of active UEs and transmit power under the assumption that ZF processing is deployed in the uplink and downlink transmissions for analytic convenience. This analysis is then extended to the imperfect CSI case and to symmetric multi-cell scenarios. These expressions provide valuable design understandings on the interaction between systems parameters, propagation environment, and different components of the power consumption model. Analytical results are assumed only for ZF with perfect channel state information (CSI) to compute closed-form expression for the optimal number of UEs, number of BS antennas, and transmit power. Numerical results are provided (a) for all the investigated schemes with perfect CSI and in a single-cell scenario; (b) for ZF with imperfect CSI, and in a multi-cell scenario. The simulation results show that (a) an LS MIMO with 100 – 200 BS antennas are the correct number of antennas for energy efficiency maximisation; (b) these number of BS antennas should serve number of active UEs of the same size; (c) since the circuit power increases the transmit power should increase with number of BS antennas; (d) the radiated power antenna is in the range of 10-100 mW and decreases with number of BS antennas; (e) ZF processing provides the highest EE in all the scenarios due to active interference-suppression at affordable complexity. Therefore, these are highly relevant results that prove LS MIMO is the technique to achieve high EE in future cellular networks.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London
URI: http://bura.brunel.ac.uk/handle/2438/16052
Appears in Collections:Dept of Electronic and Computer Engineering Theses

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